TRIM通过时空剪枝加速3D高斯扩散模型生成，提升效率且不降质量。
TRIM: Scalable 3D Gaussian Diffusion Inference with Temporal and Spatial Trimming
- 用轻量选择器提前筛选高质量高斯点，减少采样轨迹长度。
- 引入实例掩码去噪，剔除冗余背景点，每步计算量大幅降低。
- 适合需要快速生成高质量3D内容的场景，如实时渲染与交互应用。
近期的3D高斯扩散模型因大量高斯原语导致去噪和后处理耗时，生成速度慢且难以扩展。为提升效率，我们提出TRIM（轨迹缩减与实例掩码去噪），一种无需重新训练的推理加速方法。该方法结合时空剪枝策略，在不牺牲输出质量的前提下支持推理阶段的可扩展性。通过轻量选择器评估多噪声样本生成的潜在高斯点，实现早期轨迹缩减；同时引入实例掩码去噪，通过过滤冗余背景区域，减少每步去噪中的可学习高斯原语数量，显著降低计算开销。大量实验表明，TRIM在保持高质量生成的同时大幅提升推理效率。源代码见：https://github.com/zeyuanyin/TRIM。
原文摘要 · Abstract (English)
Recent advances in 3D Gaussian diffusion models suffer from time-intensive denoising and post-denoising processing due to the massive number of Gaussian primitives, resulting in slow generation and limited scalability along sampling trajectories. To improve the efficiency of 3D diffusion models, we propose $\textbf{TRIM}$ ($\textbf{T}$rajectory $\textbf{R}$eduction and $\textbf{I}$nstance $\textbf{M}$ask denoising), a post-training approach that incorporates both temporal and spatial trimming strategies, to accelerate inference without compromising output quality while supporting the inference-time scaling for Gaussian diffusion models. Instead of scaling denoising trajectories in a costly end-to-end manner, we develop a lightweight selector model to evaluate latent Gaussian primitives derived from multiple sampled noises, enabling early trajectory reduction by selecting candidates with high-quality potential. Furthermore, we introduce instance mask denoising to prune learnable Gaussian primitives by filtering out redundant background regions, reducing inference computation at each denoising step. Extensive experiments and analysis demonstrate that TRIM significantly improves both the efficiency and quality of 3D generation. Source code is available at $\href{https://github.com/zeyuanyin/TRIM}{link}$.
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